Co-founder & AI Engineer

Building an AI-driven platform that connects sustainable energy projects with top vendors and funding schemes. Leveraging multi-agent systems and predictive models to match project needs with real-time opportunities in clean tech procurement.

Tech stack: LangGraph, FastAPI, React, Supabase, pgvector, LLM-driven agents

Agentic AI Systems

Designing and deploying modular AI agents for structured data extraction, feasibility analysis, and compliance mapping. Powering intelligent assistants for energy procurement and infrastructure-matching workflows.

AI & ML Projects

Python-based predictive analytics applied across domains:

•    Loan Default Prediction (Finance): Logistic regression, decision trees, and ANN for classification — projected $1.8M annual savings (model code)

•    Lead Conversion Prediction (EdTech): Random forest achieving 89% recall for lead conversion insights (GitHub)

•    Order Data Analysis (Food Tech): Exploratory data analysis and visualizations to optimize delivery operations (notebook)

Macro Modelling & Civilisational Cycles

Developing forecasting tools grounded on long-term macroeconomic cycles, historical trends, and evolutionary dynamics. Bridging the gap between history-informed models and data-driven analytics.

Studies & Background

Fernando holds a PhD in philosophy and intellectual history, with a focus on economics and data science. Over the past decade, he has worked as a macroeconomic analyst for UK-based consulting firms and contributed to research in the humanities and social sciences across Italy and Argentina.

He has developed an interdisciplinary approach to the study of civilisation, integrating humanistic inquiry with data-driven methods. He has completed advanced studies at institutions such as the Massachusetts Institute of Technology (MIT), SDA Bocconi, University of Murcia / University of Exeter, Universidad Nacional de Quilmes, and Universidad Nacional de Tucumán.